Examining Access to Primary Care for People With Opioid Use Disorder in Ontario, Canada
Bibliographic record
Abstract
Importance: People with opioid use disorder are less likely than others to have a primary care physician. Objective: To determine if family physicians are less likely to accept people with opioid use disorder as new patients than people with diabetes. Design, Setting, and Participants: This randomized clinical trial used an audit design to survey new patient intake at randomly selected family physicians in Ontario, Canada. Eligible physicians were independent practitioners allowed to prescribe opioids who were located in an office within 50 km of a population center greater than 20 000 people. A patient actor made unannounced telephone calls to family physicians asking for a new patient appointment. The data were analyzed in September 2021. Intervention: In the first randomly assigned scenario, the patient actor played a role of patient with diabetes in treatment with an endocrinologist. In the second scenario, the patient actor played a role of a patient with opioid use disorder undergoing methadone treatment with an addiction physician. Main Outcomes and Measures: Total offers of a new patient appointment; a secondary analysis compared the proportions of patients offered an appointment stratified by gender, population, model of care, and years in practice. Results: Of a total 383 family physicians included in analysis, a greater proportion offered a new patient appointment to a patient with diabetes (21 of 185 physicians [11.4%]) than with opioid use disorder (8 of 198 physicians [4.0%]) (absolute difference, 7.4%; 95% CI, 2.0 to 12.6; P = .007). Physicians with more than 20 years in practice were almost 13 times less likely to offer an appointment to a patient with opioid use disorder compared with diabetes (1 of 108 physicians [0.9%] vs 10 of 84 physicians [11.9%]; absolute difference, 11.0; 95% CI, 3.8 to 18.1; P = .001). Women were almost 5 times less likely (3 of 111 physicians [2.7%] vs 14 of 114 physicians [12.3%]; absolute difference, 9.6%; 95% CI, 2.4 to 16.3; P = .007) to offer an appointment to a patient with opioid use disorder than with diabetes. Conclusions and Relevance: In this randomized clinical trial, family physicians were less likely to offer a new patient appointment to a patient with opioid use disorder compared with a patient with diabetes. Potential health system solutions to this disparity include strengthening policies for accepting new patients, improved compensation, and clinician anti-oppression training. Trial Registration: ClinicalTrials.gov Identifier: NCT05484609.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".